Overview

Dataset statistics

Number of variables56
Number of observations1050
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory467.6 KiB
Average record size in memory456.0 B

Variable types

Numeric20
Categorical36

Warnings

vote has constant value "1.0" Constant
fav_impeach is highly correlated with fav_senacquittalHigh correlation
fav_senacquittal is highly correlated with fav_impeachHigh correlation
health is highly correlated with voteHigh correlation
fav_deathpen is highly correlated with voteHigh correlation
lgbtlaw is highly correlated with voteHigh correlation
armedforces is highly correlated with voteHigh correlation
better_health is highly correlated with voteHigh correlation
education is highly correlated with voteHigh correlation
gov_responsive is highly correlated with voteHigh correlation
vote is highly correlated with health and 34 other fieldsHigh correlation
party_reg is highly correlated with voteHigh correlation
better_economy is highly correlated with voteHigh correlation
gov_corrup is highly correlated with voteHigh correlation
satisfied is highly correlated with voteHigh correlation
people_trusted is highly correlated with voteHigh correlation
spouse_edu is highly correlated with voteHigh correlation
trust_media is highly correlated with voteHigh correlation
inc_gap is highly correlated with voteHigh correlation
union is highly correlated with voteHigh correlation
stayhome is highly correlated with voteHigh correlation
better_taxes is highly correlated with voteHigh correlation
children is highly correlated with voteHigh correlation
gov_interests is highly correlated with voteHigh correlation
gov_trust is highly correlated with voteHigh correlation
gov_climate is highly correlated with voteHigh correlation
region is highly correlated with voteHigh correlation
interest_campaign is highly correlated with voteHigh correlation
interest_politics is highly correlated with voteHigh correlation
gov_waste is highly correlated with voteHigh correlation
better_covid is highly correlated with voteHigh correlation
marital is highly correlated with voteHigh correlation
primary_voter is highly correlated with voteHigh correlation
party_salience is highly correlated with voteHigh correlation
better_immigratino is highly correlated with voteHigh correlation
better_environment is highly correlated with voteHigh correlation
covid_gov is highly correlated with voteHigh correlation
covid_reopen is highly correlated with voteHigh correlation
getcovid is highly correlated with voteHigh correlation
caseid has unique values Unique

Reproduction

Analysis started2021-09-21 01:54:36.887006
Analysis finished2021-09-21 01:55:37.480392
Duration1 minute and 0.59 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

caseid
Real number (ℝ≥0)

UNIQUE

Distinct1050
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281256.2276
Minimum200046
Maximum517218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:37.588928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum200046
5-th percentile203433.3
Q1215471.5
median232169.5
Q3343695.25
95-th percentile427105.35
Maximum517218
Range317172
Interquartile range (IQR)128223.75

Descriptive statistics

Standard deviation78812.40643
Coefficient of variation (CV)0.2802156848
Kurtosis-0.9008797514
Mean281256.2276
Median Absolute Deviation (MAD)27802
Skewness0.6969379237
Sum295319039
Variance6211395408
MonotonicityNot monotonic
2021-09-20T21:55:37.715546image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2088731
 
0.1%
3371771
 
0.1%
4305021
 
0.1%
3338471
 
0.1%
2061741
 
0.1%
2084391
 
0.1%
3663971
 
0.1%
4046261
 
0.1%
3267331
 
0.1%
3228921
 
0.1%
Other values (1040)1040
99.0%
ValueCountFrequency (%)
2000461
0.1%
2001901
0.1%
2003811
0.1%
2004041
0.1%
2005031
0.1%
ValueCountFrequency (%)
5172181
0.1%
5047311
0.1%
4650031
0.1%
4649941
0.1%
4639911
0.1%

varstrat
Real number (ℝ≥0)

Distinct50
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:37.846570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25.5
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.43774644
Coefficient of variation (CV)0.5661861351
Kurtosis-1.200962786
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum26775
Variance208.4485224
MonotonicityNot monotonic
2021-09-20T21:55:37.988942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3121
 
2.0%
2321
 
2.0%
4321
 
2.0%
321
 
2.0%
4021
 
2.0%
3321
 
2.0%
1221
 
2.0%
2621
 
2.0%
5021
 
2.0%
821
 
2.0%
Other values (40)840
80.0%
ValueCountFrequency (%)
121
2.0%
221
2.0%
321
2.0%
421
2.0%
521
2.0%
ValueCountFrequency (%)
5021
2.0%
4921
2.0%
4821
2.0%
4721
2.0%
4621
2.0%

interest_politics
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
484 
1.0
299 
3.0
177 
4.0
90 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
2.0484
46.1%
1.0299
28.5%
3.0177
 
16.9%
4.090
 
8.6%
2021-09-20T21:55:38.224948image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:38.294985image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0484
46.1%
1.0299
28.5%
3.0177
 
16.9%
4.090
 
8.6%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2484
15.4%
1299
 
9.5%
3177
 
5.6%
490
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2484
23.0%
1299
 
14.2%
3177
 
8.4%
490
 
4.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2484
15.4%
1299
 
9.5%
3177
 
5.6%
490
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2484
15.4%
1299
 
9.5%
3177
 
5.6%
490
 
2.9%

interest_campaign
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
631 
2.0
330 
3.0
89 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.0631
60.1%
2.0330
31.4%
3.089
 
8.5%
2021-09-20T21:55:38.463919image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:38.530135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0631
60.1%
2.0330
31.4%
3.089
 
8.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1631
20.0%
2330
 
10.5%
389
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1631
30.0%
2330
 
15.7%
389
 
4.2%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1631
20.0%
2330
 
10.5%
389
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1631
20.0%
2330
 
10.5%
389
 
2.8%

state_reg
Real number (ℝ≥0)

Distinct31
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.92857143
Minimum2
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:38.600935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q18
median23
Q336
95-th percentile42
Maximum54
Range52
Interquartile range (IQR)28

Descriptive statistics

Standard deviation14.16791724
Coefficient of variation (CV)0.6179153935
Kurtosis-1.301374401
Mean22.92857143
Median Absolute Deviation (MAD)14
Skewness0.1772090358
Sum24075
Variance200.7298788
MonotonicityNot monotonic
2021-09-20T21:55:38.717513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6163
15.5%
1288
 
8.4%
3682
 
7.8%
4271
 
6.8%
3765
 
6.2%
2560
 
5.7%
853
 
5.0%
3450
 
4.8%
2447
 
4.5%
446
 
4.4%
Other values (21)325
31.0%
ValueCountFrequency (%)
22
 
0.2%
446
 
4.4%
516
 
1.5%
6163
15.5%
853
 
5.0%
ValueCountFrequency (%)
5414
 
1.3%
4923
 
2.2%
465
 
0.5%
441
 
0.1%
4271
6.8%

party_reg
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
436 
2.0
360 
4.0
251 
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0
ValueCountFrequency (%)
1.0436
41.5%
2.0360
34.3%
4.0251
23.9%
5.03
 
0.3%
2021-09-20T21:55:38.917130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:38.981921image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0436
41.5%
2.0360
34.3%
4.0251
23.9%
5.03
 
0.3%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1436
13.8%
2360
 
11.4%
4251
 
8.0%
53
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1436
20.8%
2360
 
17.1%
4251
 
12.0%
53
 
0.1%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1436
13.8%
2360
 
11.4%
4251
 
8.0%
53
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1436
13.8%
2360
 
11.4%
4251
 
8.0%
53
 
0.1%

primary_voter
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
625 
2.0
425 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.0625
59.5%
2.0425
40.5%
2021-09-20T21:55:39.159321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:39.219137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0625
59.5%
2.0425
40.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1625
19.8%
2425
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1625
29.8%
2425
20.2%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1625
19.8%
2425
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1625
19.8%
2425
13.5%

pol_spectrum
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.036190476
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:39.272928image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.723671553
Coefficient of variation (CV)0.4270540659
Kurtosis-1.129840225
Mean4.036190476
Median Absolute Deviation (MAD)2
Skewness-0.01015071852
Sum4238
Variance2.971043624
MonotonicityNot monotonic
2021-09-20T21:55:39.350669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4241
23.0%
6224
21.3%
2215
20.5%
3125
11.9%
5121
11.5%
766
 
6.3%
158
 
5.5%
ValueCountFrequency (%)
158
 
5.5%
2215
20.5%
3125
11.9%
4241
23.0%
5121
11.5%
ValueCountFrequency (%)
766
 
6.3%
6224
21.3%
5121
11.5%
4241
23.0%
3125
11.9%

party_id
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.852380952
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:39.432360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.353448892
Coefficient of variation (CV)0.6109076233
Kurtosis-1.570453582
Mean3.852380952
Median Absolute Deviation (MAD)2
Skewness0.08334183143
Sum4045
Variance5.538721685
MonotonicityNot monotonic
2021-09-20T21:55:39.509103image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1288
27.4%
7234
22.3%
5121
11.5%
3118
11.2%
6113
 
10.8%
2111
 
10.6%
465
 
6.2%
ValueCountFrequency (%)
1288
27.4%
2111
 
10.6%
3118
11.2%
465
 
6.2%
5121
11.5%
ValueCountFrequency (%)
7234
22.3%
6113
10.8%
5121
11.5%
465
 
6.2%
3118
11.2%

party_salience
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
264 
3.0
261 
5.0
232 
4.0
166 
1.0
127 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row1.0
4th row5.0
5th row2.0
ValueCountFrequency (%)
2.0264
25.1%
3.0261
24.9%
5.0232
22.1%
4.0166
15.8%
1.0127
12.1%
2021-09-20T21:55:39.709482image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:39.775255image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0264
25.1%
3.0261
24.9%
5.0232
22.1%
4.0166
15.8%
1.0127
12.1%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2264
 
8.4%
3261
 
8.3%
5232
 
7.4%
4166
 
5.3%
1127
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2264
 
12.6%
3261
 
12.4%
5232
 
11.0%
4166
 
7.9%
1127
 
6.0%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2264
 
8.4%
3261
 
8.3%
5232
 
7.4%
4166
 
5.3%
1127
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2264
 
8.4%
3261
 
8.3%
5232
 
7.4%
4166
 
5.3%
1127
 
4.0%

gov_trust
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
4.0
501 
3.0
342 
2.0
130 
5.0
71 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0
ValueCountFrequency (%)
4.0501
47.7%
3.0342
32.6%
2.0130
 
12.4%
5.071
 
6.8%
1.06
 
0.6%
2021-09-20T21:55:39.958643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:40.025425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0501
47.7%
3.0342
32.6%
2.0130
 
12.4%
5.071
 
6.8%
1.06
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4501
15.9%
3342
 
10.9%
2130
 
4.1%
571
 
2.3%
16
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
4501
23.9%
3342
 
16.3%
2130
 
6.2%
571
 
3.4%
16
 
0.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4501
15.9%
3342
 
10.9%
2130
 
4.1%
571
 
2.3%
16
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4501
15.9%
3342
 
10.9%
2130
 
4.1%
571
 
2.3%
16
 
0.2%

gov_interests
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
917 
2.0
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0917
87.3%
2.0133
 
12.7%
2021-09-20T21:55:40.208807image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:40.270608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0917
87.3%
2.0133
 
12.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1917
29.1%
2133
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1917
43.7%
2133
 
6.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1917
29.1%
2133
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1917
29.1%
2133
 
4.2%

gov_waste
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
699 
2.0
335 
3.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0699
66.6%
2.0335
31.9%
3.016
 
1.5%
2021-09-20T21:55:40.438055image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:40.501796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0699
66.6%
2.0335
31.9%
3.016
 
1.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1699
22.2%
2335
 
10.6%
316
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1699
33.3%
2335
 
16.0%
316
 
0.8%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1699
22.2%
2335
 
10.6%
316
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1699
22.2%
2335
 
10.6%
316
 
0.5%

gov_corrup
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
3.0
393 
4.0
328 
2.0
309 
1.0
 
14
5.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row3.0
5th row2.0
ValueCountFrequency (%)
3.0393
37.4%
4.0328
31.2%
2.0309
29.4%
1.014
 
1.3%
5.06
 
0.6%
2021-09-20T21:55:41.178528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:41.253272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0393
37.4%
4.0328
31.2%
2.0309
29.4%
1.014
 
1.3%
5.06
 
0.6%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3393
 
12.5%
4328
 
10.4%
2309
 
9.8%
114
 
0.4%
56
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
3393
 
18.7%
4328
 
15.6%
2309
 
14.7%
114
 
0.7%
56
 
0.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3393
 
12.5%
4328
 
10.4%
2309
 
9.8%
114
 
0.4%
56
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3393
 
12.5%
4328
 
10.4%
2309
 
9.8%
114
 
0.4%
56
 
0.2%

people_trusted
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
615 
3.0
238 
4.0
183 
5.0
 
7
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
2.0615
58.6%
3.0238
 
22.7%
4.0183
 
17.4%
5.07
 
0.7%
1.07
 
0.7%
2021-09-20T21:55:41.467587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:41.536366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0615
58.6%
3.0238
 
22.7%
4.0183
 
17.4%
5.07
 
0.7%
1.07
 
0.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2615
19.5%
3238
 
7.6%
4183
 
5.8%
17
 
0.2%
57
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2615
29.3%
3238
 
11.3%
4183
 
8.7%
17
 
0.3%
57
 
0.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2615
19.5%
3238
 
7.6%
4183
 
5.8%
17
 
0.2%
57
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2615
19.5%
3238
 
7.6%
4183
 
5.8%
17
 
0.2%
57
 
0.2%

gov_responsive
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
571 
1.0
353 
3.0
126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
2.0571
54.4%
1.0353
33.6%
3.0126
 
12.0%
2021-09-20T21:55:41.732702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:41.808436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0571
54.4%
1.0353
33.6%
3.0126
 
12.0%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2571
18.1%
1353
 
11.2%
3126
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2571
27.2%
1353
 
16.8%
3126
 
6.0%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2571
18.1%
1353
 
11.2%
3126
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2571
18.1%
1353
 
11.2%
3126
 
4.0%

better_economy
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
5.0
314 
1.0
220 
2.0
184 
3.0
173 
4.0
159 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row1.0
4th row1.0
5th row3.0
ValueCountFrequency (%)
5.0314
29.9%
1.0220
21.0%
2.0184
17.5%
3.0173
16.5%
4.0159
15.1%
2021-09-20T21:55:41.995827image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:42.063599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0314
29.9%
1.0220
21.0%
2.0184
17.5%
3.0173
16.5%
4.0159
15.1%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5314
 
10.0%
1220
 
7.0%
2184
 
5.8%
3173
 
5.5%
4159
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
5314
 
15.0%
1220
 
10.5%
2184
 
8.8%
3173
 
8.2%
4159
 
7.6%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5314
 
10.0%
1220
 
7.0%
2184
 
5.8%
3173
 
5.5%
4159
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5314
 
10.0%
1220
 
7.0%
2184
 
5.8%
3173
 
5.5%
4159
 
5.0%

better_health
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
403 
5.0
191 
2.0
169 
3.0
154 
4.0
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0403
38.4%
5.0191
18.2%
2.0169
16.1%
3.0154
 
14.7%
4.0133
 
12.7%
2021-09-20T21:55:42.243005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:42.308780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0403
38.4%
5.0191
18.2%
2.0169
16.1%
3.0154
 
14.7%
4.0133
 
12.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1403
 
12.8%
5191
 
6.1%
2169
 
5.4%
3154
 
4.9%
4133
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1403
 
19.2%
5191
 
9.1%
2169
 
8.0%
3154
 
7.3%
4133
 
6.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1403
 
12.8%
5191
 
6.1%
2169
 
5.4%
3154
 
4.9%
4133
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1403
 
12.8%
5191
 
6.1%
2169
 
5.4%
3154
 
4.9%
4133
 
4.2%

better_immigratino
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
334 
5.0
313 
2.0
194 
4.0
117 
3.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0334
31.8%
5.0313
29.8%
2.0194
18.5%
4.0117
 
11.1%
3.092
 
8.8%
2021-09-20T21:55:42.490174image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:42.555962image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0334
31.8%
5.0313
29.8%
2.0194
18.5%
4.0117
 
11.1%
3.092
 
8.8%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1334
 
10.6%
5313
 
9.9%
2194
 
6.2%
4117
 
3.7%
392
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1334
 
15.9%
5313
 
14.9%
2194
 
9.2%
4117
 
5.6%
392
 
4.4%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1334
 
10.6%
5313
 
9.9%
2194
 
6.2%
4117
 
3.7%
392
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1334
 
10.6%
5313
 
9.9%
2194
 
6.2%
4117
 
3.7%
392
 
2.9%

better_taxes
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
5.0
288 
3.0
230 
2.0
194 
1.0
193 
4.0
145 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row1.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
5.0288
27.4%
3.0230
21.9%
2.0194
18.5%
1.0193
18.4%
4.0145
13.8%
2021-09-20T21:55:42.750272image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:42.817087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0288
27.4%
3.0230
21.9%
2.0194
18.5%
1.0193
18.4%
4.0145
13.8%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5288
 
9.1%
3230
 
7.3%
2194
 
6.2%
1193
 
6.1%
4145
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
5288
 
13.7%
3230
 
11.0%
2194
 
9.2%
1193
 
9.2%
4145
 
6.9%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5288
 
9.1%
3230
 
7.3%
2194
 
6.2%
1193
 
6.1%
4145
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5288
 
9.1%
3230
 
7.3%
2194
 
6.2%
1193
 
6.1%
4145
 
4.6%

better_environment
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
464 
3.0
192 
2.0
185 
5.0
118 
4.0
91 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.0464
44.2%
3.0192
18.3%
2.0185
 
17.6%
5.0118
 
11.2%
4.091
 
8.7%
2021-09-20T21:55:43.009437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:43.075236image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0464
44.2%
3.0192
18.3%
2.0185
 
17.6%
5.0118
 
11.2%
4.091
 
8.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1464
14.7%
3192
 
6.1%
2185
 
5.9%
5118
 
3.7%
491
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1464
22.1%
3192
 
9.1%
2185
 
8.8%
5118
 
5.6%
491
 
4.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1464
14.7%
3192
 
6.1%
2185
 
5.9%
5118
 
3.7%
491
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1464
14.7%
3192
 
6.1%
2185
 
5.9%
5118
 
3.7%
491
 
2.9%

better_covid
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
397 
3.0
243 
5.0
177 
2.0
131 
4.0
102 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
1.0397
37.8%
3.0243
23.1%
5.0177
16.9%
2.0131
 
12.5%
4.0102
 
9.7%
2021-09-20T21:55:43.262599image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:43.329366image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0397
37.8%
3.0243
23.1%
5.0177
16.9%
2.0131
 
12.5%
4.0102
 
9.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1397
 
12.6%
3243
 
7.7%
5177
 
5.6%
2131
 
4.2%
4102
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1397
 
18.9%
3243
 
11.6%
5177
 
8.4%
2131
 
6.2%
4102
 
4.9%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1397
 
12.6%
3243
 
7.7%
5177
 
5.6%
2131
 
4.2%
4102
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1397
 
12.6%
3243
 
7.7%
5177
 
5.6%
2131
 
4.2%
4102
 
3.2%

fav_deathpen
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
409 
4.0
226 
2.0
208 
3.0
207 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row4.0
4th row4.0
5th row1.0
ValueCountFrequency (%)
1.0409
39.0%
4.0226
21.5%
2.0208
19.8%
3.0207
19.7%
2021-09-20T21:55:43.522686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:43.588498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0409
39.0%
4.0226
21.5%
2.0208
19.8%
3.0207
19.7%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1409
 
13.0%
4226
 
7.2%
2208
 
6.6%
3207
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1409
 
19.5%
4226
 
10.8%
2208
 
9.9%
3207
 
9.9%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1409
 
13.0%
4226
 
7.2%
2208
 
6.6%
3207
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1409
 
13.0%
4226
 
7.2%
2208
 
6.6%
3207
 
6.6%

stayhome
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
4.0
502 
3.0
349 
2.0
110 
1.0
89 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
4.0502
47.8%
3.0349
33.2%
2.0110
 
10.5%
1.089
 
8.5%
2021-09-20T21:55:43.762915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:43.828657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0502
47.8%
3.0349
33.2%
2.0110
 
10.5%
1.089
 
8.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4502
15.9%
3349
 
11.1%
2110
 
3.5%
189
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
4502
23.9%
3349
 
16.6%
2110
 
5.2%
189
 
4.2%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4502
15.9%
3349
 
11.1%
2110
 
3.5%
189
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4502
15.9%
3349
 
11.1%
2110
 
3.5%
189
 
2.8%

trust_media
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
302 
3.0
278 
2.0
231 
4.0
181 
5.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
1.0302
28.8%
3.0278
26.5%
2.0231
22.0%
4.0181
17.2%
5.058
 
5.5%
2021-09-20T21:55:44.010089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:44.072883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0302
28.8%
3.0278
26.5%
2.0231
22.0%
4.0181
17.2%
5.058
 
5.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1302
 
9.6%
3278
 
8.8%
2231
 
7.3%
4181
 
5.7%
558
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1302
 
14.4%
3278
 
13.2%
2231
 
11.0%
4181
 
8.6%
558
 
2.8%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1302
 
9.6%
3278
 
8.8%
2231
 
7.3%
4181
 
5.7%
558
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1302
 
9.6%
3278
 
8.8%
2231
 
7.3%
4181
 
5.7%
558
 
1.8%

corr_trump
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.603809524
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:44.140652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.858991178
Coefficient of variation (CV)0.7139505256
Kurtosis-0.6497415955
Mean2.603809524
Median Absolute Deviation (MAD)1
Skewness0.7829044432
Sum2734
Variance3.4558482
MonotonicityNot monotonic
2021-09-20T21:55:44.218352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1494
47.0%
4251
23.9%
2132
 
12.6%
679
 
7.5%
736
 
3.4%
536
 
3.4%
322
 
2.1%
ValueCountFrequency (%)
1494
47.0%
2132
 
12.6%
322
 
2.1%
4251
23.9%
536
 
3.4%
ValueCountFrequency (%)
736
 
3.4%
679
 
7.5%
536
 
3.4%
4251
23.9%
322
 
2.1%

fav_impeach
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.784761905
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:44.302111image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.714462939
Coefficient of variation (CV)0.7172083759
Kurtosis-1.810951081
Mean3.784761905
Median Absolute Deviation (MAD)3
Skewness0.1555518923
Sum3974
Variance7.368309047
MonotonicityNot monotonic
2021-09-20T21:55:44.386787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1429
40.9%
7374
35.6%
489
 
8.5%
282
 
7.8%
649
 
4.7%
516
 
1.5%
311
 
1.0%
ValueCountFrequency (%)
1429
40.9%
282
 
7.8%
311
 
1.0%
489
 
8.5%
516
 
1.5%
ValueCountFrequency (%)
7374
35.6%
649
 
4.7%
516
 
1.5%
489
 
8.5%
311
 
1.0%

fav_senacquittal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.255238095
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:44.464529image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.669532257
Coefficient of variation (CV)0.6273520299
Kurtosis-1.762876799
Mean4.255238095
Median Absolute Deviation (MAD)3
Skewness-0.1847358431
Sum4468
Variance7.126402469
MonotonicityNot monotonic
2021-09-20T21:55:44.546296image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7423
40.3%
1356
33.9%
4119
 
11.3%
679
 
7.5%
248
 
4.6%
515
 
1.4%
310
 
1.0%
ValueCountFrequency (%)
1356
33.9%
248
 
4.6%
310
 
1.0%
4119
 
11.3%
515
 
1.4%
ValueCountFrequency (%)
7423
40.3%
679
 
7.5%
515
 
1.4%
4119
 
11.3%
310
 
1.0%

covid_gov
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
5.0
572 
3.0
361 
4.0
89 
1.0
 
16
2.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0
ValueCountFrequency (%)
5.0572
54.5%
3.0361
34.4%
4.089
 
8.5%
1.016
 
1.5%
2.012
 
1.1%
2021-09-20T21:55:44.746624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:44.812399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0572
54.5%
3.0361
34.4%
4.089
 
8.5%
1.016
 
1.5%
2.012
 
1.1%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5572
18.2%
3361
 
11.5%
489
 
2.8%
116
 
0.5%
212
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
5572
27.2%
3361
 
17.2%
489
 
4.2%
116
 
0.8%
212
 
0.6%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5572
18.2%
3361
 
11.5%
489
 
2.8%
116
 
0.5%
212
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
5572
18.2%
3361
 
11.5%
489
 
2.8%
116
 
0.5%
212
 
0.4%

covid_reopen
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
3.0
518 
5.0
158 
2.0
132 
4.0
126 
1.0
116 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
3.0518
49.3%
5.0158
 
15.0%
2.0132
 
12.6%
4.0126
 
12.0%
1.0116
 
11.0%
2021-09-20T21:55:45.008748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:45.074536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0518
49.3%
5.0158
 
15.0%
2.0132
 
12.6%
4.0126
 
12.0%
1.0116
 
11.0%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3518
16.4%
5158
 
5.0%
2132
 
4.2%
4126
 
4.0%
1116
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
3518
24.7%
5158
 
7.5%
2132
 
6.3%
4126
 
6.0%
1116
 
5.5%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3518
16.4%
5158
 
5.0%
2132
 
4.2%
4126
 
4.0%
1116
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3518
16.4%
5158
 
5.0%
2132
 
4.2%
4126
 
4.0%
1116
 
3.7%

inc_gap
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
662 
3.0
201 
2.0
147 
4.0
 
30
5.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0662
63.0%
3.0201
 
19.1%
2.0147
 
14.0%
4.030
 
2.9%
5.010
 
1.0%
2021-09-20T21:55:45.262864image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:45.328685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0662
63.0%
3.0201
 
19.1%
2.0147
 
14.0%
4.030
 
2.9%
5.010
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1662
21.0%
3201
 
6.4%
2147
 
4.7%
430
 
1.0%
510
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1662
31.5%
3201
 
9.6%
2147
 
7.0%
430
 
1.4%
510
 
0.5%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1662
21.0%
3201
 
6.4%
2147
 
4.7%
430
 
1.0%
510
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1662
21.0%
3201
 
6.4%
2147
 
4.7%
430
 
1.0%
510
 
0.3%

gov_climate
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
637 
3.0
322 
2.0
90 
-9.0
 
1

Length

Max length4
Median length3
Mean length3.000952381
Min length3

Characters and Unicode

Total characters3151
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0637
60.7%
3.0322
30.7%
2.090
 
8.6%
-9.01
 
0.1%
2021-09-20T21:55:45.518005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:45.582796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0637
60.7%
3.0322
30.7%
2.090
 
8.6%
9.01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1637
20.2%
3322
 
10.2%
290
 
2.9%
-1
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.6%
Other Punctuation1050
33.3%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1637
30.3%
3322
 
15.3%
290
 
4.3%
91
 
< 0.1%
ValueCountFrequency (%)
.1050
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3151
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1637
20.2%
3322
 
10.2%
290
 
2.9%
-1
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3151
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1637
20.2%
3322
 
10.2%
290
 
2.9%
-1
 
< 0.1%
91
 
< 0.1%

samesex
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.584761905
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:45.648566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.011159569
Coefficient of variation (CV)0.5610301667
Kurtosis-1.648930435
Mean3.584761905
Median Absolute Deviation (MAD)2.5
Skewness-0.04312694886
Sum3764
Variance4.044762813
MonotonicityNot monotonic
2021-09-20T21:55:45.739327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6299
28.5%
1252
24.0%
2173
16.5%
5168
16.0%
3100
 
9.5%
458
 
5.5%
ValueCountFrequency (%)
1252
24.0%
2173
16.5%
3100
 
9.5%
458
 
5.5%
5168
16.0%
ValueCountFrequency (%)
6299
28.5%
5168
16.0%
458
 
5.5%
3100
 
9.5%
2173
16.5%

transgender
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.620952381
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:45.820038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q35.75
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.008831561
Coefficient of variation (CV)0.5547798892
Kurtosis-1.611913431
Mean3.620952381
Median Absolute Deviation (MAD)2
Skewness-0.1869972391
Sum3802
Variance4.03540424
MonotonicityNot monotonic
2021-09-20T21:55:45.914730image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1291
27.7%
6263
25.0%
5222
21.1%
4110
 
10.5%
2109
 
10.4%
355
 
5.2%
ValueCountFrequency (%)
1291
27.7%
2109
 
10.4%
355
 
5.2%
4110
 
10.5%
5222
21.1%
ValueCountFrequency (%)
6263
25.0%
5222
21.1%
4110
10.5%
355
 
5.2%
2109
10.4%

lgbtlaw
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
792 
2.0
139 
4.0
 
71
3.0
 
48

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0792
75.4%
2.0139
 
13.2%
4.071
 
6.8%
3.048
 
4.6%
2021-09-20T21:55:46.116005image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:46.181823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0792
75.4%
2.0139
 
13.2%
4.071
 
6.8%
3.048
 
4.6%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1792
25.1%
2139
 
4.4%
471
 
2.3%
348
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1792
37.7%
2139
 
6.6%
471
 
3.4%
348
 
2.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1792
25.1%
2139
 
4.4%
471
 
2.3%
348
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1792
25.1%
2139
 
4.4%
471
 
2.3%
348
 
1.5%

birthright
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.611428571
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:46.246608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.222483015
Coefficient of variation (CV)0.4819510874
Kurtosis-1.267097032
Mean4.611428571
Median Absolute Deviation (MAD)2
Skewness-0.4082462284
Sum4842
Variance4.93943075
MonotonicityNot monotonic
2021-09-20T21:55:46.328334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7334
31.8%
4235
22.4%
1166
15.8%
6158
15.0%
295
 
9.0%
537
 
3.5%
325
 
2.4%
ValueCountFrequency (%)
1166
15.8%
295
9.0%
325
 
2.4%
4235
22.4%
537
 
3.5%
ValueCountFrequency (%)
7334
31.8%
6158
15.0%
537
 
3.5%
4235
22.4%
325
 
2.4%

deportkids
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.026666667
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:46.417018image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.348392869
Coefficient of variation (CV)0.2682479183
Kurtosis2.020018739
Mean5.026666667
Median Absolute Deviation (MAD)1
Skewness-1.645065325
Sum5278
Variance1.81816333
MonotonicityNot monotonic
2021-09-20T21:55:46.517736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6520
49.5%
5299
28.5%
4109
 
10.4%
148
 
4.6%
243
 
4.1%
331
 
3.0%
ValueCountFrequency (%)
148
 
4.6%
243
 
4.1%
331
 
3.0%
4109
 
10.4%
5299
28.5%
ValueCountFrequency (%)
6520
49.5%
5299
28.5%
4109
 
10.4%
331
 
3.0%
243
 
4.1%

wall
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.363809524
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:46.597427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.5555832
Coefficient of variation (CV)0.585631244
Kurtosis-1.663480614
Mean4.363809524
Median Absolute Deviation (MAD)3
Skewness-0.2365643061
Sum4582
Variance6.531005493
MonotonicityNot monotonic
2021-09-20T21:55:46.682150image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7411
39.1%
1288
27.4%
4144
 
13.7%
687
 
8.3%
277
 
7.3%
325
 
2.4%
518
 
1.7%
ValueCountFrequency (%)
1288
27.4%
277
 
7.3%
325
 
2.4%
4144
13.7%
518
 
1.7%
ValueCountFrequency (%)
7411
39.1%
687
 
8.3%
518
 
1.7%
4144
 
13.7%
325
 
2.4%

russianinterfere
Real number (ℝ)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.787619048
Minimum-9
Maximum5
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)0.2%
Memory size16.4 KiB
2021-09-20T21:55:46.771862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1
Q11
median3
Q34.75
95-th percentile5
Maximum5
Range14
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation1.67230541
Coefficient of variation (CV)0.5999045714
Kurtosis2.961039661
Mean2.787619048
Median Absolute Deviation (MAD)2
Skewness-0.455784868
Sum2927
Variance2.796605384
MonotonicityNot monotonic
2021-09-20T21:55:46.852544image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1339
32.3%
5263
25.0%
2166
15.8%
3161
15.3%
4119
 
11.3%
-92
 
0.2%
ValueCountFrequency (%)
-92
 
0.2%
1339
32.3%
2166
15.8%
3161
15.3%
4119
 
11.3%
ValueCountFrequency (%)
5263
25.0%
4119
 
11.3%
3161
15.3%
2166
15.8%
1339
32.3%

religion
Real number (ℝ≥0)

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.211428571
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:46.941284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.785818447
Coefficient of variation (CV)0.5345594608
Kurtosis-1.200092009
Mean5.211428571
Median Absolute Deviation (MAD)3
Skewness-0.07800222875
Sum5472
Variance7.760784421
MonotonicityNot monotonic
2021-09-20T21:55:47.027997image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9230
21.9%
5226
21.5%
1155
14.8%
6128
12.2%
2124
11.8%
488
 
8.4%
860
 
5.7%
738
 
3.6%
31
 
0.1%
ValueCountFrequency (%)
1155
14.8%
2124
11.8%
31
 
0.1%
488
 
8.4%
5226
21.5%
ValueCountFrequency (%)
9230
21.9%
860
 
5.7%
738
 
3.6%
6128
12.2%
5226
21.5%

age
Real number (ℝ≥0)

Distinct60
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.21238095
Minimum21
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:47.144617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile29
Q140
median54
Q366
95-th percentile78
Maximum80
Range59
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.4045429
Coefficient of variation (CV)0.2894917052
Kurtosis-1.128886503
Mean53.21238095
Median Absolute Deviation (MAD)13
Skewness-0.02370841853
Sum55873
Variance237.2999419
MonotonicityNot monotonic
2021-09-20T21:55:47.274175image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8040
 
3.8%
6635
 
3.3%
4033
 
3.1%
6930
 
2.9%
3829
 
2.8%
6328
 
2.7%
5827
 
2.6%
3526
 
2.5%
4125
 
2.4%
7224
 
2.3%
Other values (50)753
71.7%
ValueCountFrequency (%)
212
 
0.2%
221
 
0.1%
232
 
0.2%
246
0.6%
255
0.5%
ValueCountFrequency (%)
8040
3.8%
798
 
0.8%
7811
 
1.0%
7711
 
1.0%
7611
 
1.0%

marital
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
932 
6.0
 
76
4.0
 
34
3.0
 
6
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0
ValueCountFrequency (%)
1.0932
88.8%
6.076
 
7.2%
4.034
 
3.2%
3.06
 
0.6%
5.02
 
0.2%
2021-09-20T21:55:47.496389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:47.561209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0932
88.8%
6.076
 
7.2%
4.034
 
3.2%
3.06
 
0.6%
5.02
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1932
29.6%
676
 
2.4%
434
 
1.1%
36
 
0.2%
52
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
1932
44.4%
676
 
3.6%
434
 
1.6%
36
 
0.3%
52
 
0.1%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1932
29.6%
676
 
2.4%
434
 
1.1%
36
 
0.2%
52
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
1932
29.6%
676
 
2.4%
434
 
1.1%
36
 
0.2%
52
 
0.1%

education
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
4.0
314 
3.0
307 
5.0
306 
2.0
103 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row4.0
4th row4.0
5th row4.0
ValueCountFrequency (%)
4.0314
29.9%
3.0307
29.2%
5.0306
29.1%
2.0103
 
9.8%
1.020
 
1.9%
2021-09-20T21:55:47.739623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:47.807397image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0314
29.9%
3.0307
29.2%
5.0306
29.1%
2.0103
 
9.8%
1.020
 
1.9%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4314
 
10.0%
3307
 
9.7%
5306
 
9.7%
2103
 
3.3%
120
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
4314
 
15.0%
3307
 
14.6%
5306
 
14.6%
2103
 
4.9%
120
 
1.0%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4314
 
10.0%
3307
 
9.7%
5306
 
9.7%
2103
 
3.3%
120
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4314
 
10.0%
3307
 
9.7%
5306
 
9.7%
2103
 
3.3%
120
 
0.6%

spouse_edu
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
4.0
313 
3.0
292 
5.0
263 
2.0
149 
1.0
33 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row3.0
4th row5.0
5th row4.0
ValueCountFrequency (%)
4.0313
29.8%
3.0292
27.8%
5.0263
25.0%
2.0149
14.2%
1.033
 
3.1%
2021-09-20T21:55:47.984753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:48.051570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0313
29.8%
3.0292
27.8%
5.0263
25.0%
2.0149
14.2%
1.033
 
3.1%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4313
 
9.9%
3292
 
9.3%
5263
 
8.3%
2149
 
4.7%
133
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
4313
 
14.9%
3292
 
13.9%
5263
 
12.5%
2149
 
7.1%
133
 
1.6%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4313
 
9.9%
3292
 
9.3%
5263
 
8.3%
2149
 
4.7%
133
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4313
 
9.9%
3292
 
9.3%
5263
 
8.3%
2149
 
4.7%
133
 
1.0%

armedforces
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
3.0
912 
2.0
133 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row2.0
5th row3.0
ValueCountFrequency (%)
3.0912
86.9%
2.0133
 
12.7%
1.05
 
0.5%
2021-09-20T21:55:48.226984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:48.289782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0912
86.9%
2.0133
 
12.7%
1.05
 
0.5%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3912
29.0%
2133
 
4.2%
15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
3912
43.4%
2133
 
6.3%
15
 
0.2%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3912
29.0%
2133
 
4.2%
15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
3912
29.0%
2133
 
4.2%
15
 
0.2%

labor
Real number (ℝ≥0)

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.434285714
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:48.351586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.12209892
Coefficient of variation (CV)0.8717542512
Kurtosis-0.6913530325
Mean2.434285714
Median Absolute Deviation (MAD)0
Skewness0.9803826637
Sum2556
Variance4.503303827
MonotonicityNot monotonic
2021-09-20T21:55:48.437284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1690
65.7%
5232
 
22.1%
768
 
6.5%
229
 
2.8%
616
 
1.5%
411
 
1.0%
84
 
0.4%
ValueCountFrequency (%)
1690
65.7%
229
 
2.8%
411
 
1.0%
5232
 
22.1%
616
 
1.5%
ValueCountFrequency (%)
84
 
0.4%
768
 
6.5%
616
 
1.5%
5232
22.1%
411
 
1.0%

union
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
848 
1.0
202 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0
ValueCountFrequency (%)
2.0848
80.8%
1.0202
 
19.2%
2021-09-20T21:55:48.633624image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:48.696412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0848
80.8%
1.0202
 
19.2%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2848
26.9%
1202
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2848
40.4%
1202
 
9.6%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2848
26.9%
1202
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2848
26.9%
1202
 
6.4%

ethnicity
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.55047619
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:48.750232image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.237261021
Coefficient of variation (CV)0.7979877595
Kurtosis4.504493697
Mean1.55047619
Median Absolute Deviation (MAD)0
Skewness2.324182726
Sum1628
Variance1.530814835
MonotonicityNot monotonic
2021-09-20T21:55:48.841886image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1833
79.3%
375
 
7.1%
244
 
4.2%
442
 
4.0%
634
 
3.2%
522
 
2.1%
ValueCountFrequency (%)
1833
79.3%
244
 
4.2%
375
 
7.1%
442
 
4.0%
522
 
2.1%
ValueCountFrequency (%)
634
3.2%
522
 
2.1%
442
4.0%
375
7.1%
244
4.2%

children
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
0.0
652 
2.0
168 
1.0
150 
3.0
 
54
4.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0
ValueCountFrequency (%)
0.0652
62.1%
2.0168
 
16.0%
1.0150
 
14.3%
3.054
 
5.1%
4.026
 
2.5%
2021-09-20T21:55:49.040263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:49.105049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0652
62.1%
2.0168
 
16.0%
1.0150
 
14.3%
3.054
 
5.1%
4.026
 
2.5%

Most occurring characters

ValueCountFrequency (%)
01702
54.0%
.1050
33.3%
2168
 
5.3%
1150
 
4.8%
354
 
1.7%
426
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01702
81.0%
2168
 
8.0%
1150
 
7.1%
354
 
2.6%
426
 
1.2%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
01702
54.0%
.1050
33.3%
2168
 
5.3%
1150
 
4.8%
354
 
1.7%
426
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
01702
54.0%
.1050
33.3%
2168
 
5.3%
1150
 
4.8%
354
 
1.7%
426
 
0.8%

income
Real number (ℝ≥0)

Distinct22
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.88761905
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:49.187774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median17
Q320
95-th percentile22
Maximum22
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.017351701
Coefficient of variation (CV)0.4041849595
Kurtosis-0.2719130312
Mean14.88761905
Median Absolute Deviation (MAD)4
Skewness-0.8148902935
Sum15632
Variance36.20852149
MonotonicityNot monotonic
2021-09-20T21:55:49.282453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
22113
 
10.8%
2197
 
9.2%
1791
 
8.7%
1981
 
7.7%
2075
 
7.1%
1870
 
6.7%
1062
 
5.9%
1560
 
5.7%
1660
 
5.7%
158
 
5.5%
Other values (12)283
27.0%
ValueCountFrequency (%)
158
5.5%
28
 
0.8%
38
 
0.8%
417
 
1.6%
517
 
1.6%
ValueCountFrequency (%)
22113
10.8%
2197
9.2%
2075
7.1%
1981
7.7%
1870
6.7%

health
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
427 
3.0
320 
1.0
176 
4.0
104 
5.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row4.0
ValueCountFrequency (%)
2.0427
40.7%
3.0320
30.5%
1.0176
16.8%
4.0104
 
9.9%
5.023
 
2.2%
2021-09-20T21:55:49.492749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:49.559535image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0427
40.7%
3.0320
30.5%
1.0176
16.8%
4.0104
 
9.9%
5.023
 
2.2%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2427
13.6%
3320
 
10.2%
1176
 
5.6%
4104
 
3.3%
523
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2427
20.3%
3320
 
15.2%
1176
 
8.4%
4104
 
5.0%
523
 
1.1%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2427
13.6%
3320
 
10.2%
1176
 
5.6%
4104
 
3.3%
523
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2427
13.6%
3320
 
10.2%
1176
 
5.6%
4104
 
3.3%
523
 
0.7%

getcovid
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
1019 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0
ValueCountFrequency (%)
2.01019
97.0%
1.031
 
3.0%
2021-09-20T21:55:49.729955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:49.791749image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.01019
97.0%
1.031
 
3.0%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
21019
32.3%
131
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
21019
48.5%
131
 
1.5%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
21019
32.3%
131
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
21019
32.3%
131
 
1.0%

satisfied
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2.0
457 
3.0
329 
1.0
184 
4.0
66 
5.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row4.0
4th row3.0
5th row3.0
ValueCountFrequency (%)
2.0457
43.5%
3.0329
31.3%
1.0184
17.5%
4.066
 
6.3%
5.014
 
1.3%
2021-09-20T21:55:49.957162image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:50.022943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0457
43.5%
3.0329
31.3%
1.0184
17.5%
4.066
 
6.3%
5.014
 
1.3%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2457
14.5%
3329
 
10.4%
1184
 
5.8%
466
 
2.1%
514
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
2457
21.8%
3329
 
15.7%
1184
 
8.8%
466
 
3.1%
514
 
0.7%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2457
14.5%
3329
 
10.4%
1184
 
5.8%
466
 
2.1%
514
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
2457
14.5%
3329
 
10.4%
1184
 
5.8%
466
 
2.1%
514
 
0.4%

vote
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
1.0
1050 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.01050
100.0%
2021-09-20T21:55:50.195398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:50.259163image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.01050
100.0%

Most occurring characters

ValueCountFrequency (%)
11050
33.3%
.1050
33.3%
01050
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
11050
50.0%
01050
50.0%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
11050
33.3%
.1050
33.3%
01050
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
11050
33.3%
.1050
33.3%
01050
33.3%

region
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
4.0
370 
1.0
310 
3.0
301 
2.0
69 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3150
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row3.0
4th row4.0
5th row4.0
ValueCountFrequency (%)
4.0370
35.2%
1.0310
29.5%
3.0301
28.7%
2.069
 
6.6%
2021-09-20T21:55:50.415666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-09-20T21:55:50.480403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0370
35.2%
1.0310
29.5%
3.0301
28.7%
2.069
 
6.6%

Most occurring characters

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4370
 
11.7%
1310
 
9.8%
3301
 
9.6%
269
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2100
66.7%
Other Punctuation1050
33.3%

Most frequent character per category

ValueCountFrequency (%)
01050
50.0%
4370
 
17.6%
1310
 
14.8%
3301
 
14.3%
269
 
3.3%
ValueCountFrequency (%)
.1050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3150
100.0%

Most frequent character per script

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4370
 
11.7%
1310
 
9.8%
3301
 
9.6%
269
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3150
100.0%

Most frequent character per block

ValueCountFrequency (%)
.1050
33.3%
01050
33.3%
4370
 
11.7%
1310
 
9.8%
3301
 
9.6%
269
 
2.2%

whovoted
Real number (ℝ≥0)

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.48
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2021-09-20T21:55:50.549209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6750913507
Coefficient of variation (CV)0.4561428045
Kurtosis59.27805541
Mean1.48
Median Absolute Deviation (MAD)0
Skewness4.710612071
Sum1554
Variance0.4557483317
MonotonicityNot monotonic
2021-09-20T21:55:50.631940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1586
55.8%
2449
42.8%
57
 
0.7%
35
 
0.5%
42
 
0.2%
121
 
0.1%
ValueCountFrequency (%)
1586
55.8%
2449
42.8%
35
 
0.5%
42
 
0.2%
57
 
0.7%
ValueCountFrequency (%)
121
 
0.1%
57
 
0.7%
42
 
0.2%
35
 
0.5%
2449
42.8%

Interactions

2021-09-20T21:54:51.614381image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:51.747786image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:51.859426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:51.973979image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.087952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.200755image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.396099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.516699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.629322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.741946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.854565image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:52.967187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.080814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.191964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.307914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.420319image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.535337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.642734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.752013image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.863414image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:53.974088image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.086396image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.194045image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.299805image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.406256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.509914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.617557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.721252image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.826988image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:54.935290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.038999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.141632image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.248540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.364154image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.530605image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.721961image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:55.927836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.035525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.140179image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.250765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.354446image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.461098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.567704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.673378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.780035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.889618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:56.995266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.097971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.203569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.308269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.413865image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.519554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.625231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.728814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.830483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:57.934133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.034831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.138488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.253067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.360735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.468339image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.580016image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.689650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.797306image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:58.904912image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.013554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.122153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.231797image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.338477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.447115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.557702image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.670317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:54:59.779985image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.026177image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.135796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.240445image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.349099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.461680image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.569360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.680979image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.790569image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:00.902197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.011865image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.124452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.230155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.338772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.451394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.559010image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.666674image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.777303image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.886904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:01.994619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.102239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.205836image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.306554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.412188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.524815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.632451image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.738100image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.847743image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:02.953336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.061021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.171648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.278282image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.385934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.494574image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.601169image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.708856image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.817491image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:03.931113image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.039735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.149368image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.255017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.360667image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.467314image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.586872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.695558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:04.803148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.076239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.181885image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.287568image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.394170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.499872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.610503image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.720133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.830765image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:05.938383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.052969image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.168636image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.277259image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.388887image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.495532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.599140image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.705829image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.824404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:06.936067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.043701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.151350image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.259962image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.371555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.487210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.595857image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.703457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.812125image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:07.917783image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.028405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.138026image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.254612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.365274image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.475899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.583551image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.687195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.793798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:08.901497image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.006123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.108785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.215452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.324075image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.429705image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.535364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.643015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.744660image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.850317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:09.951972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.050634image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.154285image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.264929image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.370576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.476219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.578896image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.678541image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.781200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.888838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:10.993483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:11.310431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:11.414073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:11.518735image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-09-20T21:55:11.623334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-09-20T21:55:33.852554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-09-20T21:55:50.825318image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-20T21:55:51.766138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-20T21:55:52.738882image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-20T21:55:54.260795image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-20T21:55:55.156794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-20T21:55:34.231313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-20T21:55:36.801347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

caseidvarstratinterest_politicsinterest_campaignstate_regparty_regprimary_voterpol_spectrumparty_idparty_saliencegov_trustgov_interestsgov_wastegov_corruppeople_trustedgov_responsivebetter_economybetter_healthbetter_immigratinobetter_taxesbetter_environmentbetter_covidfav_deathpenstayhometrust_mediacorr_trumpfav_impeachfav_senacquittalcovid_govcovid_reopeninc_gapgov_climatesamesextransgenderlgbtlawbirthrightdeportkidswallrussianinterferereligionagemaritaleducationspouse_eduarmedforceslaborunionethnicitychildrenincomehealthgetcovidsatisfiedvoteregionwhovoted
0200046.029.02.03.06.02.02.03.06.03.03.01.02.04.02.01.04.03.03.04.03.03.01.03.04.02.02.06.04.02.03.03.05.02.01.02.05.06.03.05.041.01.03.03.03.01.02.04.01.07.03.02.01.01.04.01.0
1200985.029.01.01.042.01.01.02.01.05.03.01.01.04.02.01.02.01.01.01.01.01.01.04.04.01.01.07.05.03.01.01.04.06.01.02.06.06.01.06.080.01.05.05.02.05.01.01.00.016.02.02.01.01.01.01.0
2203427.029.01.01.024.01.01.02.01.01.04.01.01.03.02.01.01.01.01.01.01.01.04.04.04.01.01.07.05.03.01.01.06.06.01.07.06.07.01.09.062.01.04.03.03.01.02.01.00.018.03.02.04.01.03.01.0
3207344.029.01.01.06.01.01.02.01.05.04.01.01.03.02.02.01.01.01.02.01.01.04.03.02.01.01.07.05.02.01.01.04.06.01.07.06.07.01.09.068.01.04.05.02.05.01.01.00.019.03.02.03.01.04.01.0
4210678.029.02.02.016.04.02.02.03.02.04.01.01.02.03.03.03.01.01.03.02.02.01.03.02.01.01.07.05.03.01.01.06.05.01.07.06.07.02.01.040.04.04.04.03.01.02.01.02.012.04.02.03.01.04.01.0
5210869.029.01.02.031.04.02.04.05.01.04.01.01.03.04.03.04.01.02.02.01.01.01.04.02.01.01.07.05.01.01.01.06.01.01.06.05.07.01.05.050.01.04.04.03.01.02.01.02.018.03.02.04.01.02.01.0
6213974.029.02.02.036.01.02.04.01.02.01.01.02.04.02.01.03.03.03.03.03.03.03.04.04.02.01.07.04.03.03.01.05.01.01.04.05.02.04.01.051.01.02.03.03.04.01.02.00.08.03.02.02.01.01.01.0
7218146.029.03.02.06.01.01.02.02.03.04.01.01.02.02.02.03.01.01.03.01.02.02.03.03.03.01.06.05.02.01.01.03.06.01.06.06.07.01.09.042.01.05.04.03.07.02.01.02.019.03.02.03.01.04.01.0
8218924.029.04.02.06.01.01.02.01.04.04.01.02.04.02.02.01.01.01.01.02.01.04.04.03.02.02.06.05.03.01.01.06.06.01.07.06.07.02.09.041.01.05.05.03.01.02.01.01.015.03.02.01.01.04.01.0
9222688.029.02.01.012.02.02.02.02.04.04.01.01.02.03.02.01.01.01.02.01.01.01.03.02.01.01.07.05.02.01.01.01.05.01.01.05.07.02.02.055.01.03.03.03.01.02.01.00.04.03.02.03.01.03.01.0

Last rows

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1040414999.050.03.01.08.04.02.04.02.03.03.01.01.03.02.02.02.01.01.01.01.01.01.04.03.02.01.07.05.01.01.01.06.06.01.07.06.07.01.05.043.01.04.02.03.01.01.03.01.021.03.02.02.01.04.01.0
1041419000.050.01.01.08.02.01.06.07.01.04.01.01.03.02.01.05.04.05.05.04.05.01.03.01.06.07.01.03.01.03.03.01.01.01.01.05.01.05.04.067.01.05.05.02.05.02.01.00.019.02.02.01.01.04.02.0
1042428998.050.01.01.031.02.01.06.07.01.04.01.01.02.03.02.05.04.05.04.04.04.01.04.01.04.07.01.03.03.04.02.01.01.02.04.04.01.05.06.062.01.03.03.03.05.02.01.00.08.02.02.02.01.02.02.0
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1048464994.050.02.01.036.02.01.05.07.05.03.01.02.03.02.02.04.03.04.04.03.03.02.04.02.05.07.01.03.03.02.03.02.01.01.04.05.02.03.05.039.01.03.03.02.01.02.01.04.022.02.01.02.01.01.02.0
1049465003.050.02.02.041.02.01.06.07.02.02.02.02.04.02.01.05.05.05.05.03.04.01.04.03.07.07.01.03.03.03.03.01.05.02.01.06.01.03.09.059.01.03.04.03.05.02.04.00.015.02.02.02.01.04.02.0